Penerapan Metode Agglomerative Clustering Untuk Segmentasi Data Dalam Lingkungan Big Data - Paskal Arienda Epindonta Ginting; Risky Immanuel Situmorang; Muhammad Raihansyah Lubis; Raja Ansel Hartama Sihombing; Arnita Piliang (Jurnal Sistem Informasi Dan Informatika, 2026)
The exponential growth of data in the digital era has increased the need for analytical methods capable of handling Big Data characteristics. This study examines the application of Agglomerative Hierarchical Clustering (AHC) for data segmentation using two datasets: (1) an Iris dataset of 24 samples with 8 morphological attributes, and (2) an e-commerce transaction dataset of 10 customer records. Ward linkage was selected based on literature evidence of its superiority. Results on the Iris dataset yielded 3 optimal clusters with a Silhouette Score of 0.4196 and an Adjusted Rand Index of 0.3635, achieving 70.83% classification accuracy. In the e-commerce dataset, three customer segments were formed: premium, middle-tier, and passive customers. These findings confirm AHC as an effective multidimensional data segmentation method.